CustomLabs
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Capabilities

We ship the boring parts well.

Demos are easy. Production is the work. We build the unglamorous parts: retrieval, guardrails, evals, observability. That's what keeps the impressive parts holding up six months after launch.

01

Agentic workflows

Agents that call your APIs and update your systems of record. They carry a multi-step task to completion. Every tool call is scoped and every plan is inspectable. Every workflow keeps a deterministic path for when the model shouldn't be the one deciding.

How we build it
  • Explicit tool-use contracts: each tool declares its own inputs and side effects. Declared failure modes stop the agent improvising its way into a bad call
  • Deterministic fallbacks for the steps that must be right every time (payments, routing, filing), with the model handling judgment and rules code handling execution
  • Human-in-the-loop checkpoints on irreversible or high-cost actions, gating the agent rather than trusting it blindly
  • Replayable run logs so any agent decision can be re-run step by step to see exactly what happened and why
agent.run(plan, tools)
02

Conversational interfaces

Chat and voice front ends that answer from your data. We ground every response in retrieval over your own content. We enforce what the model may and may not say. We log the full exchange, so you can trace any answer back to its source.

How we build it
  • Hybrid search (keyword + vector) with re-ranking over your knowledge base, so retrieval quality doesn't degrade as content grows
  • GuardrailsGuardrails are the checks that keep an LLM or agent inside acceptable bounds in production. layer that constrains tone and scope, and blocks disallowed topics before a response ever reaches the user
  • Full audit trail: every query, retrieved passage, and generated response logged for compliance and debugging
  • Human-in-the-loop escalation for low-confidence or high-stakes turns, with a clean handoff to a live operator
chat.respond(query, ctx)
03

Document & data extraction

Turning invoices, contracts, forms, and long-form documents into structured data you can put straight into a database or downstream system. Output is schema-constrained rather than freeform, so it validates instead of merely looking plausible.

How we build it
  • Schema-constrained decoding: the model is forced into a defined JSON schema per document type, eliminating malformed or partial output
  • Per-field confidence thresholds, so low-confidence extractions are flagged rather than silently passed downstream
  • Human-in-the-loop review queues for anything below threshold, with reviewers correcting inline and feeding corrections back
  • Golden-set regression testing against known documents before any prompt or schema change ships
extract(doc, schema)
04

Evaluation & observability

The instrumentation that turns "the model seems fine" into a number you can track release over release. We build eval suitesAn eval suite is a versioned set of test cases that measures whether outputs are good. against your real use cases and capture full traces of every production call. Both surface in dashboards your team actually looks at.

How we build it
  • Task-specific eval suites built from real production examples, run against every prompt or model change before it ships
  • Span-level trace pipelines capturing every retrieval, tool call, and model invocation in a request
  • Dashboards tracking quality, latency, and cost trends over time, not just point-in-time snapshots
  • Regression gates in CI that block a deploy when eval scores drop below an agreed threshold
evals.run(suite, model)
The stack we build on All open standards
L4 · Interface
ChatVoiceInternal appsAPIsWebhooks
L3 · Reasoning
Agent runtimeRoutingTool-useMemory
L2 · Retrieval
Vector storeHybrid searchRe-rankingCaching
L1 · Foundation
AnthropicOpenAIOpen-weightSelf-hosted
L0 · Operations
Eval suitesTracingCost guardrailsIncident response
Eval methodology

Quality is a measured number.

How we make AI quality measurable, before we ever call a feature done.

  1. 01

    Eval suites from real cases

    Built from real production examples and the edge cases that already bit you.

  2. 02

    Regression gates in CI

    The suite runs on every prompt, model, or retrieval change and fails the build when quality, cost, or latency regress.

  3. 03

    Dashboards & observability

    Span-level traces plus quality, latency, and cost trends your team actually watches over time.

See the full Eval Stack → Read the full methodology → See capability 04 · Evaluation & observability

Source: https://customlabs.io/capabilities/

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